Even if you haven't heard of data described as a "time-series," you've probably seen examples out in the wild. As the name suggests, a time-series is a representation of an event over a period of time. That could mean representing many different changes: your application usage data, such as error rates over time or the growing number of activations per day.
C# logs can be an invaluable resource for optimizing application performance and debugging errors. But it’s not easy to extract the full potential of your logs if they’re not providing enough context around each error, or if they’re written in a format that’s difficult to parse.
RSA 2019 is finally here and we’re super-excited to participate this year in this great gathering of security experts where we will be demoing Logz.io Security Analytics — our new app for helping organizations combat security threats and meet compliance requirements.
There’s a lot of confusion surrounding the differences between structured and unstructured data. To better understand why, let’s review which data formats the industry currently is using, and some of the challenges they pose. Simply put, structured data typically refers to highly organized, stored information that is efficiently and easily searchable. Unstructured data is not.
As applications are getting more complex, it’s becoming harder to deliver high-quality applications. Tools likeJavaScript has come a long way in recent years. Browsers are becoming more robust and machines are growing more powerful. Pair this with the recent development of Node.js for execution of JavaScript on servers, and you can understand why JavaScript has exploded in popularity.
The days when you could simply SSH into a server and perform a fancy grep are long gone. If you’re reading this article, chances are either you are looking to move from that obsolete approach to a centralized logging approach with a log management tool, or you are looking for an alternative log management tool to replace your existing solution. Problem is, there are so many different tools out there, making a choice can be overwhelming. So how do you pick the right solution?
When I first joined this startup that makes an observability platform, I was a front-end Javascript developer who had never ssh’ed into production–I didn’t even know what tracing or monitoring or metrics were, let alone what it meant for logs to be structured or how they could be useful to me. But within a couple months I joined the on-call rotation, and now share responsibility in our services along with the rest of my team.
Views may seem straightforward at first, but they hide a lot of power. On a very basic level, a view is a shortcut to a specific search query or filter. You can use views to display only a subset of logs, create alerts and graphs, export specific events, and even embed your log event feed on another website. In this post, we’ll present several tips and tricks for making the most out of views.
In a previous post, we introduced a new integration with Microsoft Azure that makes it easy to ship Azure logs and metrics into Logz.io using a ready-made deployment template. Once in Logz.io, this data can be analyzed using the advanced analytics tools Logz.io has to offer — you can query the data, create visualizations and dashboards, and create alerts to get notified when something out of the ordinary occurs.
